






"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_SCREAMING_SNAKE_CASE   :							str          =		logging.getLogger(__name__)






@dataclass
class 	a		:
    SCREAMING_SNAKE_CASE		:	str		=					field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default=__snake_case    , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    , )
    SCREAMING_SNAKE_CASE		:	bool		=					field(default=__snake_case    , metadata={"""help""": """Whether tp freeze the encoder."""}						)
    SCREAMING_SNAKE_CASE		:	bool		=					field(default=__snake_case    , metadata={"""help""": """Whether  to freeze the embeddings."""}						)






@dataclass
class 	a		:
    SCREAMING_SNAKE_CASE		:	str		=					field(
        metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(
        default="""summarization"""    , metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=1_0_2_4    , metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=1_2_8    , metadata={
            """help""": (
                """The maximum total sequence length for target text after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=1_4_2    , metadata={
            """help""": (
                """The maximum total sequence length for validation target text after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded. """
                """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
                """during ``evaluate`` and ``predict``."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(
        default=1_4_2    , metadata={
            """help""": (
                """The maximum total sequence length for test target text after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }    , )
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(default=-1    , metadata={"""help""": """# training examples. -1 means use all."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(default=-1    , metadata={"""help""": """# validation examples. -1 means use all."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(default=-1    , metadata={"""help""": """# test examples. -1 means use all."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(default=__snake_case    , metadata={"""help""": """Source language id for translation."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[str]		=					field(default=__snake_case    , metadata={"""help""": """Target language id for translation."""}						)
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					field(default=__snake_case    , metadata={"""help""": """# num_beams to use for evaluation."""}						)
    SCREAMING_SNAKE_CASE		:	bool		=					field(
        default=__snake_case    , metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}    , )







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Any						,							_lowerCamelCase			:						List[Any]					) ->							List[str]:
  logger.info(F'''***** {split} metrics *****'''					)
  for key in sorted(metrics.keys()					):
    logger.info(F'''  {key} = {metrics[key]}'''					)
  save_json(_lowerCamelCase						,							os.path.join(_lowerCamelCase						,							F'''{split}_results.json'''					)					)







def 	lowerCamelCase__			(				) ->							Optional[Any]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  lowerCamelCase_       	=		HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

  if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		parser.parse_args_into_dataclasses()

  check_output_dir(_lowerCamelCase					)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,							datefmt='%m/%d/%Y %H:%M:%S'						,							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,							)
  logger.warning(
      'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'						,							training_args.local_rank						,							training_args.device						,							training_args.n_gpu						,							bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					)						,							training_args.fpaa						,							)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank					):
    transformers.utils.logging.set_verbosity_info()
  logger.info('Training/evaluation parameters %s'						,							_lowerCamelCase					)

  # Set seed
  set_seed(training_args.seed					)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowerCamelCase_       	=		AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							)

  lowerCamelCase_       	=		('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
  for p in extra_model_params:
    if getattr(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					):
      assert hasattr(_lowerCamelCase						,							_lowerCamelCase					), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
      setattr(_lowerCamelCase						,							_lowerCamelCase						,							getattr(_lowerCamelCase						,							_lowerCamelCase					)					)

  lowerCamelCase_       	=		AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							)
  lowerCamelCase_       	=		AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path						,							from_tf='.ckpt' in model_args.model_name_or_path						,							config=_lowerCamelCase						,							cache_dir=model_args.cache_dir						,							)

  # use task specific params
  use_task_specific_params(_lowerCamelCase						,							data_args.task					)

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    lowerCamelCase_       	=		model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(_lowerCamelCase						,							(MBartTokenizer, MBartTokenizerFast)					):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCamelCase_       	=		tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

  if model_args.freeze_embeds:
    freeze_embeds(_lowerCamelCase					)
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()					)
    assert_all_frozen(model.get_encoder()					)

  lowerCamelCase_       	=		SeqaSeqDataset

  # Get datasets
  lowerCamelCase_       	=		(
      dataset_class(
          _lowerCamelCase						,							type_path='train'						,							data_dir=data_args.data_dir						,							n_obs=data_args.n_train						,							max_target_length=data_args.max_target_length						,							max_source_length=data_args.max_source_length						,							prefix=model.config.prefix or ''						,							)
      if training_args.do_train
      else None
  )
  lowerCamelCase_       	=		(
      dataset_class(
          _lowerCamelCase						,							type_path='val'						,							data_dir=data_args.data_dir						,							n_obs=data_args.n_val						,							max_target_length=data_args.val_max_target_length						,							max_source_length=data_args.max_source_length						,							prefix=model.config.prefix or ''						,							)
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  lowerCamelCase_       	=		(
      dataset_class(
          _lowerCamelCase						,							type_path='test'						,							data_dir=data_args.data_dir						,							n_obs=data_args.n_test						,							max_target_length=data_args.test_max_target_length						,							max_source_length=data_args.max_source_length						,							prefix=model.config.prefix or ''						,							)
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  lowerCamelCase_       	=		(
      build_compute_metrics_fn(data_args.task						,							_lowerCamelCase					) if training_args.predict_with_generate else None
  )
  lowerCamelCase_       	=		SeqaSeqTrainer(
      model=_lowerCamelCase						,							args=_lowerCamelCase						,							data_args=_lowerCamelCase						,							train_dataset=_lowerCamelCase						,							eval_dataset=_lowerCamelCase						,							data_collator=SeqaSeqDataCollator(
          _lowerCamelCase						,							_lowerCamelCase						,							model.config.decoder_start_token_id						,							training_args.tpu_num_cores					)						,							compute_metrics=_lowerCamelCase						,							tokenizer=_lowerCamelCase						,							)

  lowerCamelCase_       	=		{}
  # Training
  if training_args.do_train:
    logger.info('*** Train ***'					)

    lowerCamelCase_       	=		trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
    lowerCamelCase_       	=		train_result.metrics
    lowerCamelCase_       	=		data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics('train'						,							_lowerCamelCase						,							training_args.output_dir					)
      all_metrics.update(_lowerCamelCase					)

      # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
      trainer.state.save_to_json(os.path.join(training_args.output_dir						,							'trainer_state.json'					)					)

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
  if training_args.do_eval:
    logger.info('*** Evaluate ***'					)

    lowerCamelCase_       	=		trainer.evaluate(metric_key_prefix='val'					)
    lowerCamelCase_       	=		data_args.n_val
    lowerCamelCase_       	=		round(metrics['val_loss']						,							4					)

    if trainer.is_world_process_zero():
      handle_metrics('val'						,							_lowerCamelCase						,							training_args.output_dir					)
      all_metrics.update(_lowerCamelCase					)

  if training_args.do_predict:
    logger.info('*** Predict ***'					)

    lowerCamelCase_       	=		trainer.predict(test_dataset=_lowerCamelCase						,							metric_key_prefix='test'					)
    lowerCamelCase_       	=		test_output.metrics
    lowerCamelCase_       	=		data_args.n_test

    if trainer.is_world_process_zero():
      lowerCamelCase_       	=		round(metrics['test_loss']						,							4					)
      handle_metrics('test'						,							_lowerCamelCase						,							training_args.output_dir					)
      all_metrics.update(_lowerCamelCase					)

      if training_args.predict_with_generate:
        lowerCamelCase_       	=		tokenizer.batch_decode(
            test_output.predictions						,							skip_special_tokens=_lowerCamelCase						,							clean_up_tokenization_spaces=_lowerCamelCase					)
        lowerCamelCase_       	=		lmap(str.strip						,							_lowerCamelCase					)
        write_txt_file(_lowerCamelCase						,							os.path.join(training_args.output_dir						,							'test_generations.txt'					)					)

  if trainer.is_world_process_zero():
    save_json(_lowerCamelCase						,							os.path.join(training_args.output_dir						,							'all_results.json'					)					)

  return all_metrics







def 	lowerCamelCase__			(				_lowerCamelCase			:						int					) ->							Dict:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()












"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




